train_all.py

from transformers import GPT2LMHeadModel, AutoTokenizer
from transformers import AdamW, get_scheduler, set_seed
from datasets import load_dataset
from accelerate import Accelerator
import datasets, transformers
from huggingface_hub import Repository

from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.tensorboard import SummaryWriter
from argparse import Namespace
import torch
import logging
import wandb


class ConstantLengthDataset(IterableDataset):
    def __init__(
        self,
        tokenizer,
        dataset,
        da_type,
        seq_length=1024,
        num_of_sequences=1024,
        chars_per_token=5.2,
    ):
        self.tokenizer = tokenizer
        self.concat_token_id = tokenizer.bos_token_id
        self.dataset = dataset
        self.da_type = da_type
        self.seq_length = seq_length
        self.input_characters = seq_length * chars_per_token * num_of_sequences

    def __iter__(self):
        iterator = iter(self.dataset[f"{self.da_type}"])
        more_examples = True
        while more_examples:
            buffer, buffer_len = [], 0
            while True:
                if buffer_len >= self.input_characters:
                    break
                try:
                    buffer.append(next(iterator)["text"])
                    buffer_len += len(buffer[-1])
                except StopIteration:
                    more_examples = False
                    break
            tokenized_inputs = tokenizer(buffer, truncation=False)["input_ids"]
            all_token_ids = []
            for tokenized_input in tokenized_inputs:
                all_token_ids.extend(tokenized_input + [self.concat_token_id])
            for i in range(0, len(all_token_ids), self.seq_length):
                input_ids = all_token_ids[i : i + self.seq_length]
                if len(input_ids) == self.seq_length:
                    yield torch.tensor(input_ids)


def setup_logging(project_name):
    logger = logging.getLogger(__name__)
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
        handlers=[
            logging.FileHandler(f"log/debug_{accelerator.process_index}.log"),
            logging.StreamHandler(),
        ],
    )
    if accelerator.is_main_process:  # we only want to setup logging once
        wandb.init(project=project_name, config=args)
        run_name = wandb.run.name
        tb_writer = SummaryWriter()
        tb_writer.add_hparams(vars(args), {"0": 0})
        logger.setLevel(logging.INFO)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        tb_writer = None
        run_name = ""
        logger.setLevel(logging.ERROR)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, tb_writer, run_name


def create_dataloaders(args):
    ds_kwargs = {"streaming": True}
    train_data = load_dataset(
        "text", data_files={"train": ["train_raw.txt"]}, **ds_kwargs
    )
    train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
    valid_data = load_dataset(
        "text", data_files={"valid": ["valid_raw.txt"]}, **ds_kwargs
    )
    train_dataset = ConstantLengthDataset(
        tokenizer, train_data, da_type="train", seq_length=args.seq_length
    )
    valid_dataset = ConstantLengthDataset(
        tokenizer, valid_data, da_type="valid", seq_length=args.seq_length
    )
    train_dataloader = DataLoader(train_dataset, batch_size=args.train_batch_size)
    eval_dataloader = DataLoader(valid_dataset, batch_size=args.valid_batch_size)
    return train_dataloader, eval_dataloader


def get_grouped_params(model, args, no_decay=["bias", "LayerNorm.weight"]):
    params_with_wd, params_without_wd = [], []
    for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay):
            params_without_wd.append(p)
        else:
            params_with_wd.append(p)
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]


def log_metrics(step, metrics):
    logger.info(f"Step {step}: {metrics}")
    if accelerator.is_main_process:
        wandb.log(metrics)
        [tb_writer.add_scalar(k, v, step) for k, v in metrics.items()]


def evaluate(args):
    model.eval()
    losses = []
    for step, batch in enumerate(eval_dataloader):
        with torch.no_grad():
            outputs = model(batch, labels=batch)
        loss = outputs.loss.repeat(args.valid_batch_size)
        losses.append(accelerator.gather(loss))
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    loss = torch.mean(torch.cat(losses))
    try:
        perplexity = torch.exp(loss)
    except OverflowError:
        perplexity = float("inf")
    return loss.item(), perplexity.item()


# Accelerator
accelerator = Accelerator(dispatch_batches=True)
acc_state = {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
# Hyperparameters
project_name = "krupalkp/custom_llm-small"
dataset_name = "../codeparrot"
config = {
    "train_batch_size": 2,
    "valid_batch_size": 2,
    "weight_decay": 0.1,
    "shuffle_buffer": 1_000,
    "learning_rate": 2e-4,
    "lr_scheduler_type": "cosine",
    "num_warmup_steps": 750,
    "gradient_accumulation_steps": 16,
    "max_train_steps": 50_000,
    "max_eval_steps": -1,
    "seq_length": 1024,
    "seed": 1,
    "save_checkpoint_steps": 50_000,
}
args = Namespace(**config, **acc_state)
samples_per_step = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Logging
logger, tb_writer, run_name = setup_logging(project_name.split("/")[1])
logger.info(accelerator.state)

# Load model and tokenizer
if accelerator.is_main_process:
    hf_repo = Repository("./", clone_from=project_name, revision=run_name)
model = GPT2LMHeadModel.from_pretrained("./")
tokenizer = AutoTokenizer.from_pretrained("./")

# Load dataset and dataloader
train_dataloader, eval_dataloader = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
optimizer = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lr_scheduler = get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)


def get_lr():
    return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# Train model
model.train()
completed_steps = 0
for step, batch in enumerate(train_dataloader, start=1):
    loss = model(batch, labels=batch, use_cache=False).loss
    log_metrics(
        step,
        {
            "lr": get_lr(),
            "samples": step * samples_per_step,
            "steps": completed_steps,
            "loss/train": loss.item(),
        },
    )
    loss = loss / args.gradient_accumulation_steps
    accelerator.backward(loss)
    if step % args.gradient_accumulation_steps == 0:
        accelerator.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
        logger.info("Evaluating and saving model checkpoint")
        eval_loss, perplexity = evaluate(args)
        log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        if accelerator.is_main_process:
            unwrapped_model.save_pretrained("./")
            hf_repo.push_to_hub(commit_message=f"step {step}")
        model.train()
    if completed_steps >= args.max_train_steps:
        break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
eval_loss, perplexity = evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
if accelerator.is_main_process:
    unwrapped_model.save_pretrained("./")
    hf_repo.push_to_hub(commit_message=f"final model")